Reducing Risk Rather than Trying to Beat the Market: A New Look at Optimal Portfolios under the Mean-Variance Model

The optimization of investment portfolio is the key to financial risk investment. In this study, the investment portfolio was optimized by removing the noise of covariance matrix in the mean-variance model. Firstly, the mean-variance model and noise in covariance matrix were briefly introduced. Then, the correlation matrix was denoised by KR method (Sharifi S, Grane M, Shamaie A) from random matrix theory (RMT). Then, an example was given to analyze the application of the method in financial stock investment portfolio. It was found that the stability of the matrix was improved and the minimum risk was reduced after denoising. The study of minimum risk under different M values and stock number suggested that calculating the optimal value of M and stock number based on RMT could achieve optimal financial risk investment portfolio result. It shows that RMT has a good effect on portfolio optimization and is worth promoting widely.

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Why Do We Reject the Mean-Variance Model?

Scandinavian Journal of Economics ◽

10.2307/3440834 ◽

1995 ◽

Vol 97 (1) ◽

pp. 137 ◽

Cited By ~ 1

Author(s):

W. Jos Jansen

Keyword(s):

Variance Model ◽

The Mean ◽

Mean Variance

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MEAN-VARIANCE VERSUS MEAN–EXPECTED SHORTFALL MODELS: AN APPLICATION TO WHEAT VARIETY SELECTION

Journal of Agricultural and Applied Economics ◽

10.1017/aae.2016.8 ◽

2016 ◽

Vol 48 (2) ◽

pp. 148-172 ◽

Cited By ~ 3

Author(s):

KUNLAPATH SUKCHAROEN ◽

DAVID LEATHAM

Keyword(s):

Risk Measure ◽

Management Strategies ◽

Wheat Variety ◽

Expected Shortfall ◽

Wheat Varieties ◽

Variance Model ◽

Variety Selection ◽

Risk Management Strategies ◽

The Mean ◽

Mean Variance

AbstractOne of the most popular risk management strategies for wheat producers is varietal diversification. Previous studies proposed a mean-variance model as a tool to optimally select wheat varieties. However, this study suggests that the mean–expected shortfall (ES) model (which is based on a downside risk measure) may be a better tool because variance is not a correct risk measure when the distribution of wheat variety yields is multivariate nonnormal. Results based on data from Texas Blacklands confirm our conjecture that the mean-ES framework performs better in term of selecting wheat varieties than the mean-variance method.

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Estratégia Long-Short, Neutra ao Mercado, e Index Tracking Baseadas em Portfólios Cointegrados

Brazilian Review of Finance ◽

10.12660/rbfin.v8n4.2010.1534 ◽

2010 ◽

Vol 8 (4) ◽

pp. 469

Author(s):

João Frois Caldeira ◽

Marcelo Savino Portugal

Keyword(s):

Ad Hoc ◽

Tracking Error ◽

Covariance Matrices ◽

Return Level ◽

Index Tracking ◽

Optimal Portfolios ◽

The Mean ◽

Efficient Allocations ◽

Mean Variance ◽

Tracking Portfolios

The traditional models to optimize portfolios based on mean-variance analysis aim to determine the portfolio weights that minimize the variance for a certain return level. The covariance matrices used to optimize are difficult to estimate and ad hoc methods often need to be applied to limit or smooth the mean-variance efficient allocations recommended by the model. Although the method is efficient, the tracking error isn’t certainly stationary, so the portfolio can get distant from the benchmark, requiring frequent re-balancements. This work uses cointegration methodology to devise two quantitative strategies: index tracking and long-short market neutral. We aim to design optimal portfolios acquiring the asset prices’ co-movements. The results show that the devise of index tracking portfolios using cointegration generates goods results, replicating the benchmark’s return and volatility. The long-short strategy generated stable returns under several market circumstances, presenting low volatility.

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